Development of a Deep-Learning Model for Diagnosing Lumbar Spinal Stenosis Based on CT Images.

STUDY DESIGN: Retrospective study. OBJECTIVES: This study aimed to develop an initial deep-learning (DL) model based on computerized tomography (CT) scans for diagnosing lumbar spinal stenosis. SUMMARY OF BACKGROUND DATA: Magnetic resonance imaging is commonly used for diagnosing lumbar spinal stenosis due to its high soft tissue resolution, but CT is more portable, cost-effective, and has wider regional coverage. Using DL models to improve the accuracy of CT diagnosis can effectively reduce missed diagnoses and misdiagnoses in clinical practice. MATERIALS AND METHODS: Axial lumbar spine CT scans obtained between March 2022 and September 2023 were included. The data set was divided into a training set (62.3%), a validation set (22.9%), and a control set (14.8%). All data were labeled by two spine surgeons using the widely accepted grading system for lumbar spinal stenosis. The training and validation sets were used to annotate the regions of interest by the two spine surgeons. First, a region of interest detection model and a convolutional neural network classifier were trained using the training set. After training, the model was preliminarily evaluated using a validation set. Finally, the performance of the DL model was evaluated on the control set, and a comparison was made between the model and the classification performance of specialists with varying levels of experience. RESULTS: The central stenosis grading accuracies of DL Model Version 1 and DL Model Version 2 were 88% and 83%, respectively. The lateral recess grading accuracies of DL Model Version 1 and DL Model Version 2 were 75% and 71%, respectively. CONCLUSIONS: Our preliminarily developed DL system for assessing the degree of lumbar spinal stenosis in CT, including the central canal and lateral recess, has shown similar accuracy to experienced specialist physicians. This holds great value for further development and clinical application.

Complications and radiographic changes after implantation of interspinous process devices: average eight-year follow-up.

PURPOSE: This study aims to evaluate complications, clinical outcomes, and radiographic results following Coflex implantation. METHODS: We retrospectively studied 66 patients who had decompressive surgery combined with Coflex implantation to treat lumbar spinal stenosis. All imaging data were collected and examined for imaging changes. Clinical outcomes, included Oswestry Disability Index (ODI), back and leg visual analog scale (VAS) scores, were evaluated before surgery, six months after surgery and at the last follow-up. The number of complications occurring after five years of follow-up was counted. All reoperation cases were meticulously recorded. RESULTS: 66 patients were followed up for 5-14 years. The VAS and ODI scores were significantly improved compared with baseline. Heterotopic Ossification (HO) was detectable in 59 (89.4%). 26 (39.4%) patients had osteolysis at the contact site of Coflex with the spinous process. Coflex loosening was detected in 39 (60%) patients. Spinous process anastomosis was found in 34 (51.5%) patients. There was a statistically significant difference in the VAS score of back pain between patients with and without spinous process anastomosis. Nine cases of lumbar spinal restenosis were observed, and prosthesis fracture was observed in one case. CONCLUSION: Our study identified various imaging changes after Coflex implantation, and majority of them did not affect clinical outcomes. The majority of patients had HO, but osteolysis and Coflex loosening were relatively rare. The VAS score for back pain of these patients was higher if they have spinous process anastomosis. After five-year follow-up, we found lumbar spinal restenosis and prosthesis fracture cases.

Online study on the co-pyrolysis of coal and corn with vacuum ultraviolet photoionization mass spectrometry.

With the aim to support the experimental tests in a circulating fluidized bed pilot plant, the pyrolysis processes of coal, corn, and coal-corn blend have been studied with an online pyrolysis photoionization time-of-flight mass spectrometry (Py-PI-TOFMS). The mass spectra at different temperatures (300-800°C) as well as time-evolved profiles of selected species were measured. The pyrolysis products such as alkanes, alkenes, phenols, aromatics, as well as nitrogen- and sulfur-containing species were detected. As temperature rises, the relative ion intensities of high molecular weight products tend to decrease, while those of aromatics increase significantly. During the co-pyrolysis, coal can promote the reaction temperature of cellulose in corn. Time-evolved profiles demonstrate that coal can affect pyrolysis rate of cellulose, hemicellulose, and lignin of corn in blend. This work shows that Py-PI-TOFMS is a powerful approach to permit a better understanding of the mechanisms underlying the co-pyrolysis of coal and biomass.